用多智能体系统提升重症监护决策准确性和透明度
Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance
- 分模块智能体分别处理检验、生命体征和临床背景数据
- 多智能体系统死亡预测准确率59%，住院时长误差4.37天
- 适合医疗AI可信决策场景，兼顾准确性与可解释性
数据驱动的医学方法中，将伦理化管理与可解释的人工智能整合进临床决策支持系统（CDSS）对确保可靠有效的患者照护至关重要。本文对比了新型代理系统设计，采用模块化智能体分析实验室结果、生命体征和临床背景，并进行预测与验证。我们在eICU数据库上实现该系统，先运行实验室分析、仅生命体征解释和上下文推理智能体，再将记忆共享给集成智能体、预测智能体、透明性智能体及验证智能体。结果显示，多智能体系统（MAS）在死亡预测准确率（59%）和平均住院时长（LOS）误差（4.37天）上优于单智能体系统（SAS，分别为56%和5.82天）。然而，SAS的透明性评分（86.21）略高于MAS（85.5）。本研究表明，基于智能体的框架不仅提升了过程透明度与预测准确性，还强化了重症监护环境中可信的AI辅助决策。
原文摘要 · Abstract (English)
Recent advances in the data-driven medicine approach, which integrates ethically managed and explainable artificial intelligence into clinical decision support systems (CDSS), are critical to ensure reliable and effective patient care. This paper focuses on comparing novel agent system designs that use modular agents to analyze laboratory results, vital signs, and clinical context, and to predict and validate results. We implement our agent system with the eICU database, including running lab analysis, vitals-only interpreters, and contextual reasoners agents first, then sharing the memory into the integration agent, prediction agent, transparency agent, and a validation agent. Our results suggest that the multi-agent system (MAS) performed better than the single-agent system (SAS) with mortality prediction accuracy (59\%, 56\%) and the mean error for length of stay (LOS)(4.37 days, 5.82 days), respectively. However, the transparency score for the SAS (86.21) is slightly better than the transparency score for MAS (85.5). Finally, this study suggests that our agent-based framework not only improves process transparency and prediction accuracy but also strengthens trustworthy AI-assisted decision support in an intensive care setting.
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